{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:QTSNU6Y4GDNR35HYEO2NBVAKVL","short_pith_number":"pith:QTSNU6Y4","schema_version":"1.0","canonical_sha256":"84e4da7b1c30db1df4f823b4d0d40aaadb3be0369eff4832357f9603eb6d3a8a","source":{"kind":"arxiv","id":"2007.00973","version":2},"attestation_state":"computed","paper":{"title":"Learning to search efficiently for causally near-optimal treatments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Fredrik D. Johansson, Omer Gottesman, Samuel H{\\aa}kansson, Viktor Lindblom","submitted_at":"2020-07-02T09:17:48Z","abstract_excerpt":"Finding an effective medical treatment often requires a search by trial and error. Making this search more efficient by minimizing the number of unnecessary trials could lower both costs and patient suffering. We formalize this problem as learning a policy for finding a near-optimal treatment in a minimum number of trials using a causal inference framework. We give a model-based dynamic programming algorithm which learns from observational data while being robust to unmeasured confounding. To reduce time complexity, we suggest a greedy algorithm which bounds the near-optimality constraint. The"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2007.00973","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-07-02T09:17:48Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"75aaf245606e1109371046571ce6cf2e5709aafad5cd4d340c0dc9ecff1bc1d0","abstract_canon_sha256":"f0349d9d17de3ceb2623ebc9c9730d6b2e573526401748fd7dee57f826abafb8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:15:58.446345Z","signature_b64":"6LofMXgymszMcVAm3QgsewjjycmtLt4A8HCYgJDj1v+FZlyshTs9WroZ8sSrxNPtfpmTe0hpTaNfyw/cBxBYCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"84e4da7b1c30db1df4f823b4d0d40aaadb3be0369eff4832357f9603eb6d3a8a","last_reissued_at":"2026-07-05T02:15:58.445866Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:15:58.445866Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning to search efficiently for causally near-optimal treatments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Fredrik D. Johansson, Omer Gottesman, Samuel H{\\aa}kansson, Viktor Lindblom","submitted_at":"2020-07-02T09:17:48Z","abstract_excerpt":"Finding an effective medical treatment often requires a search by trial and error. Making this search more efficient by minimizing the number of unnecessary trials could lower both costs and patient suffering. We formalize this problem as learning a policy for finding a near-optimal treatment in a minimum number of trials using a causal inference framework. We give a model-based dynamic programming algorithm which learns from observational data while being robust to unmeasured confounding. To reduce time complexity, we suggest a greedy algorithm which bounds the near-optimality constraint. The"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2007.00973","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2007.00973/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2007.00973","created_at":"2026-07-05T02:15:58.445925+00:00"},{"alias_kind":"arxiv_version","alias_value":"2007.00973v2","created_at":"2026-07-05T02:15:58.445925+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2007.00973","created_at":"2026-07-05T02:15:58.445925+00:00"},{"alias_kind":"pith_short_12","alias_value":"QTSNU6Y4GDNR","created_at":"2026-07-05T02:15:58.445925+00:00"},{"alias_kind":"pith_short_16","alias_value":"QTSNU6Y4GDNR35HY","created_at":"2026-07-05T02:15:58.445925+00:00"},{"alias_kind":"pith_short_8","alias_value":"QTSNU6Y4","created_at":"2026-07-05T02:15:58.445925+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QTSNU6Y4GDNR35HYEO2NBVAKVL","json":"https://pith.science/pith/QTSNU6Y4GDNR35HYEO2NBVAKVL.json","graph_json":"https://pith.science/api/pith-number/QTSNU6Y4GDNR35HYEO2NBVAKVL/graph.json","events_json":"https://pith.science/api/pith-number/QTSNU6Y4GDNR35HYEO2NBVAKVL/events.json","paper":"https://pith.science/paper/QTSNU6Y4"},"agent_actions":{"view_html":"https://pith.science/pith/QTSNU6Y4GDNR35HYEO2NBVAKVL","download_json":"https://pith.science/pith/QTSNU6Y4GDNR35HYEO2NBVAKVL.json","view_paper":"https://pith.science/paper/QTSNU6Y4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2007.00973&json=true","fetch_graph":"https://pith.science/api/pith-number/QTSNU6Y4GDNR35HYEO2NBVAKVL/graph.json","fetch_events":"https://pith.science/api/pith-number/QTSNU6Y4GDNR35HYEO2NBVAKVL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QTSNU6Y4GDNR35HYEO2NBVAKVL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QTSNU6Y4GDNR35HYEO2NBVAKVL/action/storage_attestation","attest_author":"https://pith.science/pith/QTSNU6Y4GDNR35HYEO2NBVAKVL/action/author_attestation","sign_citation":"https://pith.science/pith/QTSNU6Y4GDNR35HYEO2NBVAKVL/action/citation_signature","submit_replication":"https://pith.science/pith/QTSNU6Y4GDNR35HYEO2NBVAKVL/action/replication_record"}},"created_at":"2026-07-05T02:15:58.445925+00:00","updated_at":"2026-07-05T02:15:58.445925+00:00"}